First principle study of structural, electronic and vibrational properties of 3C-SiC

Kaur, Tavneet; Sinha, Manoranjan

doi:10.1063/5.0017269

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…These equations highlight that the only information necessary to calculate S(q, ω) in a monatomic system is the phonon density of states F (ω) for a given material. This quantity has been directly measured in various experiments [69][70][71] and also calculated from first-principles ab inito calculations [72][73][74]. In this paper, for Ge, we use the experimentally measured phonon density of states at 80 K [69].…”

Section: Phonons From Photon-ion Scatteringmentioning

confidence: 99%

“…In this paper, for Ge, we use the experimentally measured phonon density of states at 80 K [69]. For the other materials (Si, GaAs, SiC, and Al 2 O 3 ), we use the ab initio calculations of [72][73][74]. Ab initio calculations are expected to match exactly with the phonon density of states at 0 K. For materials considered in this paper, the phonon density of states is not expected to vary significantly with temperature, at least up to temperatures as high as room temperature.…”

Section: Phonons From Photon-ion Scatteringmentioning

confidence: 99%

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The Phonon Background from Gamma Rays in Sub-GeV Dark Matter Detectors

Berghaus,

Essig,

Hochberg

et al. 2021

Preprint

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High-energy photons with O(MeV) energies from radioactive contaminants can scatter in a solidstate target material and constitute an important low-energy background for sub-GeV dark matter direct-detection searches. This background is most noticeable for energy deposits in the 1−100 meV range due to the partially coherent scattering enhancement in the forward scattering direction. We comprehensively quantify the resulting single-and multi-phonon background in Si, Ge, GaAs, SiC, and Al2O3 target materials, which are representative of target materials of interest in low-mass dark matter searches. We use a realistic representation of the high-energy photon background, and contrast the expected background phonon spectrum with the expected dark matter signal phonon spectrum. An active veto is needed to suppress this background sufficiently in order to allow for the detection of a dark matter signal, even in well-shielded environments.

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Section: Phonons From Photon-ion Scatteringmentioning

confidence: 99%

Section: Phonons From Photon-ion Scatteringmentioning

confidence: 99%

The Phonon Background from Gamma Rays in Sub-GeV Dark Matter Detectors

Berghaus,

Essig,

Hochberg

et al. 2021

Preprint

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“…In particular, SiC has been identified from the adsorption spectroscopy of carbon-rich extrasolar planets 23,24 , which has motivated numerous experimental and computational studies of its high temperature high pressure behavior. The phase transition of SiC from the zinc blende (3C) to the rock salt (RS) phase is observed at high pressure in experiments [25][26][27][28][29][30][31] and ab initio calculations [32][33][34][35][36][37][38][39] . Empirical potentials such as Tersoff 40,41 , Vashishta 42,43 , MEAM 44 , and Gao-Weber 45 have been developed and applied in large-scale simulations for different purposes.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

et al. 2023

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Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

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“…The phase transition of SiC under extreme conditions is relevant for studying carbon-rich exoplanets 32 and superhard materials 33 , where the transition from the zinc blende (3C) to the rock salt (RS) phase is observed at high pressure from experiments 32,[34][35][36][37][38][39] and ab initio calculations [40][41][42][43][44][45][46][47] . Empirical potentials such as Tersoff 48,49 , Vashishta 50,51 , MEAM 52 , and Gao-Weber 53 have been developed and applied in large-scale simulations for different purposes.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC

Xie¹,

Vandermause²,

Ramakers³

et al. 2022

Preprint

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Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes.Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.

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First principle study of structural, electronic and vibrational properties of 3C-SiC

Cited by 7 publications

References 14 publications

The Phonon Background from Gamma Rays in Sub-GeV Dark Matter Detectors

The Phonon Background from Gamma Rays in Sub-GeV Dark Matter Detectors

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC

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